Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network

For bearing remaining useful life prediction problem, the traditional machine-learning-based methods are generally short of feature representation ability and incapable of adaptive feature extraction. Although deep-learning-based remaining useful life prediction methods proposed in recent years can effectively extract discriminative features for bearing fault, these methods tend to less consider temporal information of fault degradation process. To solve this problem, a new remaining useful life prediction approach based on deep feature representation and long short-term memory neural network is proposed in this article. First, a new criterion, named support vector data normalized correlation coefficient, is proposed to automatically divide the whole bearing life as normal state and fast degradation state. Second, deep features of bearing fault with good representation ability can be obtained from convolutional neural network by means of the marginal spectrum in Hilbert–Huang transform of raw vibration signals and health state label. Finally, by considering the temporal information of degradation process, these features are fed into a long short-term memory neural network to construct a remaining useful life prediction model. Experiments are conducted on bearing data sets of IEEE PHM Challenge 2012. The results show the significance of performance improvement of the proposed method in terms of predictive accuracy and numerical stability.